title: Enhancing genomics information retrieval through dimensional analysis.
abstract: We propose a novel dimensional analysis approach to employing meta information in order to find the relationships within the unstructured or semi-structured document/passages for improving genomics information retrieval performance. First, we make use of the auxiliary information as three basic dimensions, namely "temporal", "journal", and "author". The reference section is treated as a commensurable quantity of the three basic dimensions. Then, the sample space and subspaces are built up and a set of events are defined to meet the basic requirement of dimensional homogeneity to be commensurable quantities. After that, the classic graph analysis algorithm in the Web environments is applied on each dimension respectively to calculate the importance of each dimension. Finally, we integrate all the dimension networks and re-rank the outputs for evaluation. Our experimental results show the proposed approach is superior and promising.
authors.affiliation: Information Retrieval and Knowledge Management Research Lab, York University, Toronto, ON M3J1P3, Canada.
authors.forename: Qinmin
authors.initials: Q
authors.lastname: Hu
authors.forename: Jimmy Xiangji
authors.initials: JX
authors.lastname: Huang
mesh_terms: D023281:Genomics / Q000379:methods*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D020407:Internet